[1]} have proposed a simple convolutional network architecture, reducing the number of parameters and opportunities for processing and used when the amount of learning data is limited to estimate age and gender. Their proposed network was evaluated on the newly released audience face images, the evaluation results showed high age recognition rate compared to some other methods. The recent work of [2]} used the CNNs of the VGG16 structure that was previously trained on ImageNet for image classification, their proposed form was named the Deep EXpectation (DEX), which relies on strong facial alignment to estimate real and apparent age. Their proposed method was evaluated on the IMDB-WIKI database, the largest public dataset to date with age and gender annotations, showing state-of-the-art results. [3]} proposed a hybrid structure that included CNN, which aimed to extract features from input images; and extreme learning machine (ELM). The hybrid method is employed for gender and age classification. The proposed method was evaluated on two popular face databases; namely, MORPHII and Adience benchmarks to classify inputs into four age groups, Children, Youth, Adults and Elderly, the best success rate achieved when using Fisher-faces with Gabor filter.
